


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Union[str, Any]	   =       (EulerDiscreteScheduler,)
     __lowercase				:							str	   =       10

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		**_a:str   ):
       snake_case__						= {
           '''num_train_timesteps''': 11_00,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**_a   )
       return config

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]     ,		[0.0002, 0.002, 0.02]   ):
         self.check_over_configs(beta_start=_a     ,		beta_end=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       scheduler.set_timesteps(self.num_inference_steps   )

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case__						= sample.to(_a   )

       for i, t in enumerate(scheduler.timesteps   ):
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         snake_case__						= model(_a     ,		_a   )

         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   )
         snake_case__						= output.prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 10.0807   ) < 1e-2
       assert abs(result_mean.item() - 0.0131   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config(prediction_type='''v_prediction'''   )
       snake_case__						= scheduler_class(**_a   )

       scheduler.set_timesteps(self.num_inference_steps   )

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case__						= sample.to(_a   )

       for i, t in enumerate(scheduler.timesteps   ):
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         snake_case__						= model(_a     ,		_a   )

         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   )
         snake_case__						= output.prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 0.0002   ) < 1e-2
       assert abs(result_mean.item() - 2.2_676e-06   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       scheduler.set_timesteps(self.num_inference_steps     ,		device=_a   )

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       snake_case__						= sample.to(_a   )

       for t in scheduler.timesteps:
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         snake_case__						= model(_a     ,		_a   )

         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   )
         snake_case__						= output.prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 10.0807   ) < 1e-2
       assert abs(result_mean.item() - 0.0131   ) < 1e-3







     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a     ,		use_karras_sigmas=_a   )

       scheduler.set_timesteps(self.num_inference_steps     ,		device=_a   )

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       snake_case__						= sample.to(_a   )

       for t in scheduler.timesteps:
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         snake_case__						= model(_a     ,		_a   )

         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   )
         snake_case__						= output.prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 124.52299499511719   ) < 1e-2
       assert abs(result_mean.item() - 0.16213932633399963   ) < 1e-3





__UpperCamelCase     		=							2_5_6
# Modulus to hash a string
__UpperCamelCase     		=							1_0_0_0_0_0_3
def 							UpperCamelCase_(					_A :str					,  _A :str	)->		bool:
			UpperCamelCase__       =							len(_A	)
			UpperCamelCase__       =							len(_A	)
			if p_len > t_len:
						return False

			UpperCamelCase__       =							0
			UpperCamelCase__       =							0
			UpperCamelCase__       =							1

			# Calculating the hash of pattern and substring of text
			for i in range(_A	):
						UpperCamelCase__       =							(ord(pattern[i]	) + p_hash * alphabet_size) % modulus
						UpperCamelCase__       =							(ord(text[i]	) + text_hash * alphabet_size) % modulus
						if i == p_len - 1:
									continue
						UpperCamelCase__       =							(modulus_power * alphabet_size) % modulus

			for i in range(0					,  t_len - p_len + 1	):
						if text_hash == p_hash and text[i : i + p_len] == pattern:
									return True
						if i == t_len - p_len:
									continue
						# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
						UpperCamelCase__       =							(
						    (text_hash - ord(text[i]	) * modulus_power) * alphabet_size
						    + ord(text[i + p_len]	)
						) % modulus
			return False
def 							UpperCamelCase_(					)->		None:
			UpperCamelCase__       =							"abc1abc12"
			UpperCamelCase__       =							"alskfjaldsabc1abc1abc12k23adsfabcabc"
			UpperCamelCase__       =							"alskfjaldsk23adsfabcabc"
			assert rabin_karp(_A					,  _A	) and not rabin_karp(_A					,  _A	)

			# Test 2)
			UpperCamelCase__       =							"ABABX"
			UpperCamelCase__       =							"ABABZABABYABABX"
			assert rabin_karp(_A					,  _A	)

			# Test 3)
			UpperCamelCase__       =							"AAAB"
			UpperCamelCase__       =							"ABAAAAAB"
			assert rabin_karp(_A					,  _A	)

			# Test 4)
			UpperCamelCase__       =							"abcdabcy"
			UpperCamelCase__       =							"abcxabcdabxabcdabcdabcy"
			assert rabin_karp(_A					,  _A	)

			# Test 5)
			UpperCamelCase__       =							"Lü"
			UpperCamelCase__       =							"Lüsai"
			assert rabin_karp(_A					,  _A	)
			UpperCamelCase__       =							"Lue"
			assert not rabin_karp(_A					,  _A	)
			print("Success."	)


if __name__ == "__main__":
						test_rabin_karp()


